A time-series is a temporally ordered set of \(n\)  variables which can be denoted by \( X=\lbrace  X_t\rbrace _{1\le t \le N}\)  where \( X_t \in \mathbb {R}^n \)  refers to the \(n\) -dimensional vector of variables at an instant \(t\) . Note that the time-series is univariate if \(n=1\) , and is multivariate otherwise (\(n>1\) ). This section reviews the necessary background for a better understanding of this survey. Specifically, we start by recalling the different types of time-series anomalies according to the taxonomy of [1]}. Then, we present the usual paradigms employed for anomaly detection in time-series.
